Using private data with freedom: A cloud-assisted ID-Private data join protocol for privacy-preserving machine learning over distributed data

This research paper proposes a cloud-assisted protocol for privacy-preserving machine learning that allows AI models to be trained on distributed data (data stored in different locations) without exposing users' private information. The protocol uses ID-Private data joins, a technique that matches data from different sources while keeping sensitive details hidden from the cloud and other parties involved.